May 10, 2021

Overview of presentation

  1. Introduction to COVID-19 World Vaccine Adverse Reactions Dataset

  2. Project work flow

  3. Project methods

    3.1 Overview of important packages and verbs used

    3.2 Challenges and solutions - Load, Clean and Augment

  4. Visualizations

  5. Modeling

  6. Conclusion and discussion

Introduction

COVID-19 World Vaccine Adverse Reactions

Introduction

###COVID-19 World Vaccine Adverse Reactions

Introduction

###COVID-19 World Vaccine Adverse Reactions

PATIENTS.CSV: Contains information about the individuals that received the vaccines

## # A tibble: 3 x 35
##   VAERS_ID RECVDATE  STATE AGE_YRS CAGE_YR CAGE_MO SEX   RPT_DATE   SYMPTOM_TEXT
##   <chr>    <chr>     <chr>   <dbl>   <dbl>   <dbl> <chr> <date>     <chr>       
## 1 0916600  01/01/20… TX         33      33      NA F     NA         "Right side…
## 2 0916601  01/01/20… CA         73      73      NA F     NA         "Approximat…
## 3 0916602  01/01/20… WA         23      23      NA F     NA         "About 15 m…
## # … with 26 more variables: DIED <chr>, DATEDIED <chr>, L_THREAT <chr>,
## #   ER_VISIT <chr>, HOSPITAL <chr>, HOSPDAYS <dbl>, X_STAY <chr>,
## #   DISABLE <chr>, RECOVD <chr>, VAX_DATE <chr>, ONSET_DATE <chr>,
## #   NUMDAYS <dbl>, LAB_DATA <chr>, V_ADMINBY <chr>, V_FUNDBY <chr>,
## #   OTHER_MEDS <chr>, CUR_ILL <chr>, HISTORY <chr>, PRIOR_VAX <chr>,
## #   SPLTTYPE <chr>, FORM_VERS <dbl>, TODAYS_DATE <chr>, BIRTH_DEFECT <chr>,
## #   OFC_VISIT <chr>, ER_ED_VISIT <chr>, ALLERGIES <chr>

Dimensions:

dim(patients)
## [1] 34121    35

Introduction

###COVID-19 World Vaccine Adverse Reactions

VACCINES.CSV: Contains information about the received vaccine

## # A tibble: 3 x 8
##   VAERS_ID VAX_TYPE VAX_MANU VAX_LOT VAX_DOSE_SERIES VAX_ROUTE VAX_SITE VAX_NAME
##   <chr>    <chr>    <chr>    <chr>   <chr>           <chr>     <chr>    <chr>   
## 1 0916600  COVID19  "MODERN… 037K20A 1               IM        LA       COVID19…
## 2 0916601  COVID19  "MODERN… 025L20A 1               IM        RA       COVID19…
## 3 0916602  COVID19  "PFIZER… EL1284  1               IM        LA       COVID19…

Dimensions:

dim(vaccines)
## [1] 34630     8

Introduction

###COVID-19 World Vaccine Adverse Reactions

SYMPTOMS.CSV: Contains information about the symptoms experienced after vaccination

## # A tibble: 3 x 11
##   VAERS_ID SYMPTOM1      SYMPTOMVERSION1 SYMPTOM2   SYMPTOMVERSION2 SYMPTOM3    
##   <chr>    <chr>                   <dbl> <chr>                <dbl> <chr>       
## 1 0916600  Dysphagia                23.1 Epiglotti…            23.1 <NA>        
## 2 0916601  Anxiety                  23.1 Dyspnoea              23.1 <NA>        
## 3 0916602  Chest discom…            23.1 Dysphagia             23.1 Pain in ext…
## # … with 5 more variables: SYMPTOMVERSION3 <dbl>, SYMPTOM4 <chr>,
## #   SYMPTOMVERSION4 <dbl>, SYMPTOM5 <chr>, SYMPTOMVERSION5 <dbl>

Dimensions:

dim(symptoms)
## [1] 48110    11

Methods: Project workflow

  1. Load data sets (patients, vaccines, symptoms)
  2. Clean each data set individually
  3. Augment and merge the data sets
  4. Make visualizations
  5. Do modeling

Methods: Important packages and verbs

Load and clean

  • readr: read_csv(), write_csv()
  • dyplyr: filter(), select(), distinct(), mutate()
  • tidyr: replace_na()

Augment

  • dplyr: filter(), select(), mutate(), case_when(), arrange(), group_by(), count(), distinct(), summarise(), drop_na(), rename()
  • tidyr: pivot_longer(), pivot_wider(), inner_join(), full_join(), pluck()
  • stringr: regular expressions, str_c(), str_replace(), str_replace()

Analysis

  • ggplot: geom_bar(), geom_boxplot(), geom_tile(), geom_segment(), theme_minimal()
  • forcats: fct_reorder()
  • scales
  • patchwork
  • viridis
  • stats (?): glm(), prcomp()
  • broom: tidy(), glance()
  • purrr: map(), nest()

Methods: Dataset loading

Challenges and solutions

Patients, vaccines and symptoms datasets:

  • Multiple large files → keep them compressed as gz-files and only decompress when reading into R
  • Wrong column types automatically assigned by R → manually assign appropriate column types
  • NA strings (“NA”, “N/A”, “Unknown”, " "…) → assign NAs when loading data

Methods: Dataset cleaning

Challenges and solutions

Patients dataset:

  • Unwanted dirty/uniformative columns → select(-c(CAGE_YR, CAGE_MO, RPT_DATE … ))
  • NAs that should be interpreted as “no” → replace_na(ALLERGIES = “N”)
  • Row duplications → distinct()

Vaccines dataset:

  • Contains non-COVID19 vaccines → filter(VAX_TYPE == “COVID19”)
  • Contains vaccines of unknown manufacturer → filter(VAX_MANU != “UNKNOWN MANUFACTURER”)
  • Row duplications → distinct()
  • Duplicated IDs → add_count(VAERS_ID) %>% filter(n == 1) %>% select(-n)
  • Inconsistent naming of vaccines → rename()
  • Redundant and dirty columns → select(-c(VAX_NAME, VAX_LOT))

Symptoms dataset:

  • SYMPTOMVERSION1-5 columns are unneccessary → select(-c())

Methods: Data augmentation

Challenges and solutions

Patients data set:

  • Columns containing long string descriptions → Make tidy categorical (Y/N) variables
## # A tibble: 3 x 3
##   VAERS_ID OTHER_MEDS                     TAKES_ANTIINFLAMATORY
##   <chr>    <chr>                          <chr>                
## 1 0916983  <NA>                           N                    
## 2 0916988  Ibuprofen  PM the night before Y                    
## 3 0916996  Clobetasol, Benadryl           N
  • Dirty, redundant and uninformative columns → select(-c(ALLERGIES, OTHER_MEDS … ))

Symptoms data set:

  • Too many symptoms and dirty → extract top 20 occurring symptoms and turn them into tidy categorical (TRUE/FALSE) columns
  • Calculate total number of symptoms per patient → mutate() to add column (N_SYMPTOMS)

Methods: Data augmentation

Merging datasets

  • For visualizing, we need the wide format → inner_join(by = VAERS_ID)
  • For modelling, symptoms must be in long-format → pivot_longer() to create:
    • SYMPTOM column: top 20 symptom names
    • SYMPTOM_VALUE column: TRUE/FALSE

Methods: Analysis

Exploratory data analysis

  • Visualizations with ggplot()
  • Reduction of dimensionality (Principal Component Analysis) with prcomp()

Modelling and statistics

  • Logistic regression models with glm()
  • Proportions tests with chisq.test()

04_analysis_visualizations

04_analysis_visualizations - Age, sex and vaccine manufacturer distribution

04_analysis_visualizations - Age distribution

04_analysis_visualizations - Age manufacturer distribution

04_analysis_visualizations - Sex and vaccine manufacturer distribution

Sex distribution
SEX n
F 24070
M 8514
NA 828
Vaccine manufacturer distribution
VAX_MANU n
JANSSEN 1106
MODERNA 16253
PFIZER-BIONTECH 16053

04_analysis_visualizations - Days until onset of symptoms vs. Age Group

04_analysis_visualizations - Age/sex vs. number of symptoms

04_analysis_visualizations - Vaccine manufacturer vs. number of symptoms

04_analysis_visualizations - Age vs. types of symptoms

04_analysis_visualizations - Sex vs. types of symptoms

04_analysis_visualizations - Vaccine manufacturer vs. types of symptoms

04_analysis_regressions

04_analysis_modeling

Logistic regression: death ~ patient profile

## # A tibble: 7 x 6
##   term           estimate std.error statistic  p.value odds_ratio
##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>      <dbl>
## 1 (Intercept)    -9.34      0.161    -58.0    0         0.0000876
## 2 SEXM            0.924     0.0573    16.1    2.18e-58  2.52     
## 3 AGE_YRS         0.0915    0.00207   44.2    0         1.10     
## 4 HAS_ALLERGIESY -0.100     0.0608    -1.65   9.82e- 2  0.904    
## 5 HAS_ILLNESSY    1.10      0.0664    16.6    6.60e-62  3.01     
## 6 HAS_COVIDY     -0.117     0.148     -0.791  4.29e- 1  0.890    
## 7 HAD_COVIDY      0.00915   0.193      0.0474 9.62e- 1  1.01

04_analysis_modeling

Logistic regression: death ~ patient profile

04_analysis_modeling

Logistic regression: death ~ symptoms

## # A tibble: 20 x 6
##   term          estimate std.error statistic  p.value odds_ratio
##   <chr>            <dbl>     <dbl>     <dbl>    <dbl>      <dbl>
## 1 (Intercept)     -2.01     0.0287    -70.1  0             0.134
## 2 HEADACHETRUE    -1.67     0.156     -10.7  7.92e-27      0.188
## 3 PYREXIATRUE     -0.429    0.112      -3.82 1.34e- 4      0.651
## 4 CHILLSTRUE      -1.21     0.171      -7.11 1.17e-12      0.298
## 5 FATIGUETRUE     -0.367    0.115      -3.19 1.41e- 3      0.693
## 6 PAINTRUE        -0.913    0.153      -5.98 2.17e- 9      0.401
## 7 NAUSEATRUE      -0.621    0.139      -4.46 8.17e- 6      0.538
## 8 DIZZINESSTRUE   -2.17     0.193     -11.2  2.87e-29      0.114
## # … with 12 more rows

04_analysis_modeling

Logistic regression: death ~ symptoms

04_analysis_modeling

Many logistic regressions: each symptom ~ takes anti-inflamatory

## # A tibble: 20 x 9
##   SYMPTOM  estimate std.error statistic p.value conf.low conf.high odds_ratio
##   <chr>       <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>      <dbl>
## 1 HEADACHE  -0.170     0.0954    -1.79   0.0742   -0.361    0.0133      0.843
## 2 PYREXIA    0.0734    0.0967     0.760  0.448    -0.120    0.259       1.08 
## 3 CHILLS    -0.0727    0.103     -0.703  0.482    -0.280    0.126       0.930
## 4 FATIGUE    0.0226    0.102      0.221  0.825    -0.183    0.219       1.02 
## 5 PAIN       0.0190    0.106      0.179  0.858    -0.194    0.222       1.02 
## # … with 15 more rows, and 1 more variable: identified_as <chr>

04_analysis_modeling

Many logistic regressions: each symptom ~ takes anti-inflamatory

04_analysis_tests

04_analysis_tests

Chi-squared contingency table tests

04_analysis_clustering

04_analysis_clustering - Important tools used

Important verbs and tools used:

  • prcomp()
  • kmeans()
  • tidymodels: (used for what?)

04_analysis_clustering - PCA biplot

04_analysis_clustering - Rotation matrix

04_analysis_clustering - Scree plot

Conclusion and discussion